Last updated: 2024-01-23
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Knit directory: UPF1-FMR1/
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library(eisaR)
source("code/libraries.R")
source("/home/neuro/Documents/NMD_analysis/other_repos/DiffRAC/DiffRAC.R")
source("code/codes.R")
library(coin)
library(gplots)
library(stargazer)
library(EnsDb.Hsapiens.v86)
library(org.Hs.eg.db)txdf = transcripts(EnsDb.Hsapiens.v86, return.type="DataFrame")
tx2gene = as.data.frame(txdf[,c("tx_id","gene_id", "tx_biotype")])sampleTable= read.csv("/home/neuro/Documents/NMD_analysis/Analysis/UPF1-FMR1/data/data-stability-no-FMR1.csv", header=TRUE, sep = ",")
inputFolder="/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Stability/quantified/"salmon.files = ("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon")
salmon = list.files(salmon.files, pattern = "transcripts$", full.names = TRUE)
sample_names = gsub("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon/", "", salmon)
sample_names = gsub(".gz_transcripts", "", sample_names)
sample_names = gsub("\\_.*", "", sample_names)
md = read.csv(here::here("data/Sample_info.csv"), header= TRUE) %>%
#mutate(files = file.path(salmon, "quant.sf")) %>%
dplyr::rename("names" = "GeneWiz.ID",
"Group" = "Sample.type") %>%
mutate(Group = ifelse(Group == "MC" | Group == "FC", "Control", Group)) %>%
dplyr::select(names,everything()) %>%
mutate(names = gsub("\\_.*", "", names) )
md = md[order(match(md$names, sample_names)),]
md %<>% dplyr::filter(Group != "FMR1") %>%
dplyr::filter(names != "23-LDJ6767") %>%
dplyr::filter(names != "202")de_exonic = DESeqDataSetFromHTSeqCount( # omits special rows from htseq-count
sampleTable = sampleTable[sampleTable$ReadType=="exonic",],
directory = inputFolder,
design = ~ 1 # required parameter
)
countsEx= counts(de_exonic)
de_intron = DESeqDataSetFromHTSeqCount( # omits special rows from htseq-count
sampleTable = sampleTable[sampleTable$ReadType=="intronic",],
directory = inputFolder,
design = ~ 1 # required parameter
)
countsIn = counts(de_intron)md$Samples = colnames(countsEx)
rownames(md) = md$SamplescountsEx = countsEx[rownames(countsEx) %in% rownames(countsIn),]
countsIn = countsIn[rownames(countsIn) %in% rownames(countsEx),]diffrac_res <- DiffRAC( ~Group + Sex+ Batch,
md,
countsEx,countsIn,
"sample",
optimizeBias = T)
Initializing DiffRAC framework...
Estimating size factors and dispersions...
Optimizing the bias constant...
0.381966011250105 : 1701754.53572386
0.618033988749895 : 1862941.554938
0.76393202250021 : 1906403.68805911
0.838792542308931 : 1916870.8253327
0.898997746487396 : 1920759.5604013
0.926877442866788 : 1921397.35589651
0.937090887858652 : 1921462.15023998
0.939292836886588 : 1921464.79709878
0.93964305261623 : 1921464.86724234
0.939976399951336 : 1921464.81307705
0.93964305261623 : 1921464.86724234
The bias constant is 0.93964305261623
Re-estimating dispersion...
Fitting model parameters...res <- as.data.frame(results(diffrac_res$dds,name = "GroupUPF1.Ratio"))
res_sig = res %>% dplyr::filter(padj < 0.05)
res %<>%
rownames_to_column("ensembl_gene_id") %>%
mutate(gene = mapIds(org.Hs.eg.db, keys=ensembl_gene_id, column="SYMBOL",keytype="ENSEMBL", multiVals="first"),
entrez = mapIds(org.Hs.eg.db, keys=ensembl_gene_id, column="ENTREZID",keytype="ENSEMBL", multiVals="first")) %>% drop_na(entrez) %>%
mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated",
ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig")))DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026", "NotSig" = "#E5E5E5")
volc_upf1 = res %>%
mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated",
ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig"))) %>%
ggplot(aes(y = -log10(padj),
x = log2FoldChange,
colour = Expression,
size =-log10(padj),
label= gene)) +
geom_point(alpha = 0.8) +
# geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
# geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
scale_colour_manual(values = DEColours) + theme_classic() +
theme(axis.title.y = element_text(size = 12)) +
geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
xlim(-8.5, 8.5) + ylim(0, 7.5)
# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
# "SMG5", "SMG6",
# "UPF3A", "ATF4",
# "GADD5G")),
# aes(label=SYMBOL), position=position_dodge(width = 0.9),
# vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
#
#
# volc
my_gg = volc_upf1 + geom_point_interactive(aes(tooltip =gene, data_id = gene),
size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)Volcano plot showing distribution of differentially stabilized / destabilized genes
res <- as.data.frame(results(diffrac_res$dds,name = "GroupFRAX.Ratio"))
res_sig = res %>% dplyr::filter(padj < 0.05)
res %<>%
rownames_to_column("ensembl_gene_id") %>%
mutate(gene = mapIds(org.Hs.eg.db, keys=ensembl_gene_id, column="SYMBOL",keytype="ENSEMBL", multiVals="first"),
entrez = mapIds(org.Hs.eg.db, keys=ensembl_gene_id, column="ENTREZID",keytype="ENSEMBL", multiVals="first")) %>% drop_na(entrez) %>%
mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated",
ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig")))DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026", "NotSig" = "#E5E5E5")
volc_fmr1 = res %>%
mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated",
ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig"))) %>%
ggplot(aes(y = -log10(padj),
x = log2FoldChange,
colour = Expression,
size =-log10(padj),
label= gene)) +
geom_point(alpha = 0.8) +
# geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
# geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
scale_colour_manual(values = DEColours) + theme_classic() +
theme(axis.title.y = element_text(size = 12)) +
geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
xlim(-8.5, 8.5) + ylim(0, 7.5)
# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
# "SMG5", "SMG6",
# "UPF3A", "ATF4",
# "GADD5G")),
# aes(label=SYMBOL), position=position_dodge(width = 0.9),
# vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
#
#
# volc
my_gg = volc_fmr1 + geom_point_interactive(aes(tooltip =gene, data_id = gene),
size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)Volcano plot showing distribution of differentially stabilized / destabilized genes
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Adelaide
tzcode source: system (glibc)
attached base packages:
[1] grid stats4 tools stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] plyr_1.8.9 org.Hs.eg.db_3.18.0
[3] EnsDb.Hsapiens.v86_2.99.0 stargazer_5.2.3
[5] gplots_3.1.3 coin_1.4-3
[7] survival_3.5-7 ngsReports_2.4.0
[9] patchwork_1.2.0 AnnotationHub_3.10.0
[11] BiocFileCache_2.10.1 dbplyr_2.4.0
[13] openxlsx_4.2.5.2 ggiraph_0.8.8
[15] DT_0.31 msigdb_1.10.0
[17] GSEABase_1.64.0 graph_1.80.0
[19] annotate_1.80.0 XML_3.99-0.16
[21] pheatmap_1.0.12 ggvenn_0.1.10
[23] MetBrewer_0.2.0 ggpubr_0.6.0
[25] venn_1.12 viridis_0.6.4
[27] viridisLite_0.4.2 tximeta_1.20.2
[29] tximport_1.30.0 goseq_1.54.0
[31] geneLenDataBase_1.38.0 BiasedUrn_2.0.11
[33] org.Mm.eg.db_3.18.0 EnsDb.Mmusculus.v79_2.99.0
[35] ensembldb_2.26.0 AnnotationFilter_1.26.0
[37] GenomicFeatures_1.54.1 AnnotationDbi_1.64.1
[39] biomaRt_2.58.0 edgeR_4.0.11
[41] limma_3.58.1 DESeq2_1.42.0
[43] SummarizedExperiment_1.32.0 Biobase_2.62.0
[45] MatrixGenerics_1.14.0 matrixStats_1.2.0
[47] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5
[49] IRanges_2.36.0 S4Vectors_0.40.2
[51] BiocGenerics_0.48.1 corrplot_0.92
[53] lubridate_1.9.3 forcats_1.0.0
[55] purrr_1.0.2 readr_2.1.5
[57] tidyverse_2.0.0 stringr_1.5.1
[59] tidyr_1.3.0 scales_1.3.0
[61] data.table_1.14.10 readxl_1.4.3
[63] tibble_3.2.1 magrittr_2.0.3
[65] reshape2_1.4.4 ggplot2_3.4.4
[67] dplyr_1.1.4 eisaR_1.14.1
[69] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.3 ProtGenerics_1.34.0
[3] bitops_1.0-7 httr_1.4.7
[5] RColorBrewer_1.1-3 backports_1.4.1
[7] utf8_1.2.4 R6_2.5.1
[9] lazyeval_0.2.2 mgcv_1.9-1
[11] withr_3.0.0 prettyunits_1.2.0
[13] gridExtra_2.3 cli_3.6.2
[15] sandwich_3.1-0 labeling_0.4.3
[17] sass_0.4.8 mvtnorm_1.2-4
[19] Rsamtools_2.18.0 systemfonts_1.0.5
[21] rstudioapi_0.15.0 RSQLite_2.3.5
[23] generics_0.1.3 BiocIO_1.12.0
[25] gtools_3.9.5 car_3.1-2
[27] zip_2.3.0 GO.db_3.18.0
[29] Matrix_1.6-5 fansi_1.0.6
[31] abind_1.4-5 lifecycle_1.0.4
[33] whisker_0.4.1 multcomp_1.4-25
[35] yaml_2.3.8 carData_3.0-5
[37] SparseArray_1.2.3 blob_1.2.4
[39] promises_1.2.1 crayon_1.5.2
[41] lattice_0.22-5 KEGGREST_1.42.0
[43] pillar_1.9.0 knitr_1.45
[45] rjson_0.2.21 admisc_0.34
[47] codetools_0.2-19 glue_1.7.0
[49] getPass_0.2-4 vctrs_0.6.5
[51] png_0.1-8 cellranger_1.1.0
[53] gtable_0.3.4 cachem_1.0.8
[55] xfun_0.41 S4Arrays_1.2.0
[57] mime_0.12 libcoin_1.0-10
[59] statmod_1.5.0 interactiveDisplayBase_1.40.0
[61] ellipsis_0.3.2 TH.data_1.1-2
[63] nlme_3.1-164 bit64_4.0.5
[65] progress_1.2.3 filelock_1.0.3
[67] rprojroot_2.0.4 bslib_0.6.1
[69] KernSmooth_2.23-22 colorspace_2.1-0
[71] DBI_1.2.1 tidyselect_1.2.0
[73] processx_3.8.3 bit_4.0.5
[75] compiler_4.3.2 curl_5.2.0
[77] git2r_0.33.0 xml2_1.3.6
[79] ggdendro_0.1.23 DelayedArray_0.28.0
[81] plotly_4.10.4 rtracklayer_1.62.0
[83] caTools_1.18.2 callr_3.7.3
[85] rappdirs_0.3.3 digest_0.6.34
[87] rmarkdown_2.25 XVector_0.42.0
[89] htmltools_0.5.7 pkgconfig_2.0.3
[91] highr_0.10 fastmap_1.1.1
[93] rlang_1.1.3 htmlwidgets_1.6.4
[95] shiny_1.8.0 farver_2.1.1
[97] jquerylib_0.1.4 zoo_1.8-12
[99] jsonlite_1.8.8 BiocParallel_1.36.0
[101] RCurl_1.98-1.14 modeltools_0.2-23
[103] GenomeInfoDbData_1.2.11 munsell_0.5.0
[105] Rcpp_1.0.12 stringi_1.8.3
[107] zlibbioc_1.48.0 MASS_7.3-60.0.1
[109] parallel_4.3.2 Biostrings_2.70.1
[111] splines_4.3.2 pander_0.6.5
[113] hms_1.1.3 locfit_1.5-9.8
[115] ps_1.7.6 uuid_1.2-0
[117] ggsignif_0.6.4 BiocVersion_3.18.1
[119] evaluate_0.23 BiocManager_1.30.22
[121] tzdb_0.4.0 httpuv_1.6.13
[123] broom_1.0.5 xtable_1.8-4
[125] restfulr_0.0.15 rstatix_0.7.2
[127] later_1.3.2 memoise_2.0.1
[129] GenomicAlignments_1.38.2 timechange_0.3.0
[131] here_1.0.1